1. Automated coronary calcium scoring using deep learning with multicenter external validation
- Author
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David Eng, Christopher Chute, Nishith Khandwala, Pranav Rajpurkar, Jin Long, Sam Shleifer, Mohamed H. Khalaf, Alexander T. Sandhu, Fatima Rodriguez, David J. Maron, Saeed Seyyedi, Daniele Marin, Ilana Golub, Matthew Budoff, Felipe Kitamura, Marcelo Straus Takahashi, Ross W. Filice, Rajesh Shah, John Mongan, Kimberly Kallianos, Curtis P. Langlotz, Matthew P. Lungren, Andrew Y. Ng, and Bhavik N. Patel
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = −2.86; Cohen’s Kappa = 0.89, P
- Published
- 2021
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